CN117155778A

CN117155778A - Cloud-based algorithm configuration method, cloud server, system and storage medium

Info

Publication number: CN117155778A
Application number: CN202311125561.XA
Authority: CN
Inventors: 黎强; 王雪梅
Original assignee: Wuhan Suowei Technology Co ltd
Current assignee: Wuhan Suowei Technology Co ltd
Priority date: 2023-09-01
Filing date: 2023-09-01
Publication date: 2023-12-01

Abstract

The application discloses a cloud-based algorithm configuration method, a cloud server, a system and a storage medium. The method comprises the following steps: responding to the received algorithm selection instruction and first configuration information, wherein the first configuration information comprises the system name and the area attribute of the equipment system so as to associate the selected algorithm with the corresponding equipment system; in response to receiving second configuration information for the algorithm, the second configuration information includes a state parameter and a command configuration parameter to form a mapping relationship of the state parameter and the command configuration parameter with each terminal device in the device system. By the method, remote deployment and updating of the control algorithm in the terminal equipment are realized, so that the labor cost is reduced; meanwhile, the applicability of the algorithm is improved, and the global control targeting on the optimization of the whole system performance is realized.

Description

Cloud-based algorithm configuration method, cloud server, system and storage medium

Technical Field

The application relates to the technical field of cloud algorithm deployment, in particular to a cloud-based algorithm configuration method, a cloud server, a cloud system and a storage medium.

Background

In conventional centralized control systems, control algorithms are typically consolidated in intelligent control devices, such as Programmable Logic Controllers (PLCs) and Direct Digital Controllers (DDCs); the PLC is a digital computer widely applied to an industrial automation control system, adopts a programmable memory and a microprocessor for executing specific instructions, and realizes control of various industrial processes and equipment by writing and debugging programs; DDC is a digital control technology for building automation control system, and uses digital computer and intelligent sensor/actuator, and related software and communication protocol to implement centralized monitoring and control of lighting, air conditioning, ventilation, etc. equipment in building.

Although the algorithms of such controllers are programmable, they generally do not have the ability to be updated remotely, once the algorithm is edited and installed in the field, the updating algorithm requires personnel to go to the project field, which is inconvenient and labor-intensive; meanwhile, most of the algorithms are empirical algorithms, the adaptability is weak, the algorithms are deployed in a single controller, and only independent control of single equipment can be realized, but global control aiming at the optimization of the performance of the whole system is not realized.

Disclosure of Invention

The application mainly provides a cloud-based algorithm configuration method, a cloud server, a system and a storage medium, which are used for solving the problem that in a traditional centralized control system, a control algorithm is solidified in intelligent equipment and remote updating and upgrading cannot be realized.

In order to solve the technical problems, the application adopts a technical scheme that: a cloud-based algorithm configuration method is provided. The method comprises the following steps: responding to the received algorithm selection instruction and first configuration information, wherein the first configuration information comprises the system name and the area attribute of the equipment system so as to associate the selected algorithm with the corresponding equipment system; in response to receiving second configuration information for the algorithm, the second configuration information includes a state parameter and a command configuration parameter to form a mapping relationship of the state parameter and the command configuration parameter with each terminal device in the device system.

In the implementation process, according to the algorithm selection instruction and the first configuration information, the selected algorithm is associated with the equipment system; and receiving second configuration information of the algorithm, and mapping the state parameters and command configuration parameters in the algorithm with each terminal device in the device system. Remote deployment and updating of the control algorithm in the terminal equipment are realized, so that the labor cost is reduced; meanwhile, the applicability of the algorithm is improved, and the global control targeting on the optimization of the whole system performance is realized.

In some embodiments, before the selecting the instruction in response to the received algorithm, the method further includes:

pre-establishing an algorithm library, wherein the algorithm library comprises a plurality of algorithms, and each algorithm comprises at least one algorithm version;

the responding to the received algorithm selection instruction comprises the following steps:

in response to the selected algorithm name and algorithm version, a corresponding algorithm is determined from the algorithm library.

In the implementation process, an algorithm library is pre-established and is used for storing all the algorithm packages which are successfully developed and verified; the algorithm names and the algorithm versions are configured for each algorithm package, so that the algorithm package can be dynamically expanded and accumulated, and the algorithm is updated more flexibly; when an algorithm selection instruction is received, a corresponding algorithm is selected from an algorithm library according to the algorithm name and the algorithm version, so that convenience and accuracy of algorithm selection are improved.

In some embodiments, the cloud-based algorithm configuration method includes:

and receiving and storing an instruction for adding, updating or deleting the algorithm in the algorithm library.

In the implementation process, the algorithm in the algorithm library is added, updated or deleted, so that flexible management of the algorithm is realized, and meanwhile, the adaptability of the algorithm is enhanced.

In some embodiments, the status parameters include device attributes, static attribute numbers, and power; the command configuration parameters include a device control command and a command trigger parameter range.

In the implementation process, the state parameters are divided into the device attribute, the static attribute number and the electric quantity, so that the state of the device can be known more specifically, and the control and management can be performed better. Meanwhile, by dividing the command configuration parameters into a device control command and a command trigger parameter range, the actions of the device can be controlled more accurately, so that the performance of the device is improved.

In some embodiments, after the receiving the second configuration information for the algorithm, the method further comprises:

running the configured algorithm at the cloud; or, deploying the configured algorithm to an edge server associated with the device system.

In the implementation process, the configured algorithm is deployed on the cloud, so that remote deployment and updating of the control algorithm can be realized, inconvenience brought by a worker going to a project site is avoided, and meanwhile, the labor cost is reduced; the configured algorithm is deployed on the edge server, so that normal operation can be maintained when the terminal equipment is in an offline state.

In some embodiments, the algorithm configuration method further comprises:

monitoring the running process of the algorithm;

and sending out prompt information when detecting that the algorithm runs wrong.

In the implementation process, the running process of the algorithm is monitored, and when the running error of the algorithm is detected, prompt information is sent out; therefore, errors in the operation process of the algorithm can be found and processed in time, the operation transparency of the algorithm is improved, and meanwhile, the stable operation of the algorithm is ensured.

In some embodiments, the monitoring the operation of the algorithm includes:

acquiring operation parameters, log information and command record information of the algorithm in the operation process;

and detecting whether the information in the log information and the command record information accords with the setting of the algorithm under the operation parameters.

In the implementation process, whether the operation process of the algorithm is normal is judged by judging whether the information in the acquired log information and command record information accords with the setting of the algorithm under the operation parameters; therefore, the monitoring of the algorithm operation process can be realized, abnormal information in the algorithm operation process can be found in time, and the stability and reliability of the algorithm operation are improved.

In order to solve the technical problems, the application adopts another technical scheme that: a storage medium is provided. The storage medium stores program data which, when executed by the processor, implements the steps of any of the cloud-based algorithm configuration methods described above.

In order to solve the technical problems, the application adopts another technical scheme that: a cloud server is provided. The cloud server comprises a processor and a memory which are connected with each other, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any cloud-based algorithm configuration method are realized.

In order to solve the technical problems, the application adopts another technical scheme that: an algorithm deployment system is provided. The algorithm deployment system comprises a cloud database, a device system and the cloud server, wherein the cloud database is associated with the cloud server, and the cloud server is in communication connection with the device system.

Drawings

For a clearer description of embodiments of the application or of solutions in the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the application, from which, without the inventive effort, other drawings can be obtained for a person skilled in the art, in which:

FIG. 1 is a flowchart illustrating an embodiment of a cloud-based algorithm configuration method according to the present application;

FIG. 2 is a schematic flow chart before step 10 in the embodiment of FIG. 1;

FIG. 3 is a schematic flow chart following step 40 in the embodiment of FIG. 1;

FIG. 4 is a schematic diagram illustrating the structure of an embodiment of a storage medium according to the present application;

fig. 5 is a schematic structural diagram of an embodiment of a cloud server according to the present application;

FIG. 6 is a schematic diagram of an algorithm deployment system according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terms "first," "second," "third," and the like in embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", and "a third" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The application provides a cloud-based algorithm configuration method, referring to fig. 1, fig. 1 is a flow chart of an embodiment of the cloud-based algorithm configuration method provided by the application, and the cloud-based algorithm configuration method comprises the following steps:

step 10: and responding to the received algorithm selection instruction and the first configuration information, and associating the selected algorithm with the corresponding equipment system.

When an algorithm is deployed, clicking the deployment on an algorithm deployment interface, and sending an algorithm selection instruction to an algorithm library; the algorithm library is a cloud database, namely the database is deployed at the cloud end, and the data storage service with expandability, high reliability and high performance is provided through an internet connection mode.

When the algorithm library receives the algorithm selection instruction, determining a corresponding algorithm according to the algorithm name and the algorithm version included in the algorithm selection instruction; the first configuration information comprises a system name and an area attribute of an equipment system, wherein the system name of the equipment system comprises a plurality of names such as a multi-split air conditioning system, a water-cooling air conditioning system, an air-cooling air conditioning system, a central air conditioning system, a split air conditioning system, a precise air conditioning system and the like; the regional attribute is a geographical region deployed by each terminal device in the device system, and the geographical region is determined according to actual service, and can be a specific name of the device system, or can be a domain name of different regions of a building in which the device system is installed.

According to the system name and the region attribute in the first configuration information, associating the selected algorithm with the corresponding equipment system, determining the corresponding equipment system according to the actual project requirement, and selecting the corresponding algorithm to be associated with according to the equipment system; by associating the algorithm with the device systems, algorithm selection and optimization for different device systems is achieved.

Optionally, referring to fig. 2, before associating the selected algorithm with the corresponding device system in response to the received algorithm selection instruction and the first configuration information, the method further includes:

step 01: and establishing an algorithm library in the cloud, and uploading the algorithm package into the algorithm library.

Compared with the algorithm stored in the algorithm library of the cloud, the conventional method is that a user purchases or develops algorithm software by himself and installs and configures the algorithm software in a local computer or a local database; this approach is applicable to single users or small teams, but increases the cost of use and technical threshold for users as the number of users increases and the data size increases; secondly, the conventional method is difficult to realize the sharing and multiplexing of the algorithm, each user needs to redevelop and maintain own algorithm, which leads to waste of resources and inefficiency, and meanwhile, the method is difficult to ensure the security and privacy of the algorithm.

According to the application, the algorithm library is established in the cloud, is a written general program code set and is used for helping a user to realize a specific task, so that the programming time is saved, and the working efficiency of a programmer is improved; after the algorithm library is established, a user can add, update or delete the algorithm package in a dynamic uploading mode, so that the management of the user on the algorithm is greatly facilitated, the sharing and multiplexing of the algorithm are realized, and repeated development and maintenance are avoided.

Step 02: an algorithm name and an algorithm version are configured for each algorithm package.

After the algorithm library is established in the cloud, continuously adding new algorithm packages into the algorithm library, wherein each algorithm package consists of an execution program of the algorithm and configuration information of the algorithm, and the executable program supports the traditional empirical algorithm and also supports an AI neural network algorithm; the traditional experience algorithm is based on human experience and heuristic rule, the algorithm is usually obtained through observation and experiment, has definite rules and steps, has limited processing capacity on data, and generally has no universality and adaptivity; the AI neural network algorithm is an algorithm based on artificial intelligence and a neural network, and realizes distributed representation and abstract reasoning of data by establishing nodes and connections similar to neurons, thereby having stronger data processing capability and applicability.

Every time an algorithm package is added into an algorithm library, an algorithm name and an algorithm version are required to be configured for the algorithm package, and after terminal equipment in an equipment system changes, the algorithm which is configured for the first time is not applicable, so that a developer needs to update the algorithm according to the condition of equipment change; when the updated algorithm package is uploaded to the algorithm library, the algorithm package with the same name is configured into different algorithm versions for convenient management, so that the reliability and traceability of the algorithm can be ensured; meanwhile, different algorithm versions can be configured according to the version models of the terminal devices in different device systems, so that flexible configuration of the algorithm is realized, and the adaptability of the algorithm is improved.

Step 20: and in response to receiving the second configuration information of the algorithm, forming a mapping relation between related parameters in the second configuration information and each terminal device in the device system.

The second configuration information includes a status parameter and a command configuration parameter. The state parameters comprise equipment attributes, static attribute numbers and electric quantity, wherein the equipment attributes are functional attributes corresponding to each terminal equipment in the equipment system, and can comprise a plurality of attributes or one attribute, such as temperature, humidity or illumination intensity; the static attribute number is used for configuring the power attribute of each terminal device, so that the power of all the terminal devices in the device system can be controlled simultaneously, and the power of one device can be controlled independently; the electric quantity corresponds to energy consumption information of each terminal device in the device system.

The command configuration parameters comprise a device control command and a command triggering parameter range, wherein the device control command is used for controlling each terminal device in the device system to execute corresponding actions, and can simultaneously control all the terminal devices in the device system and can also independently control one device; the command triggering parameter is defined for the range of the device attribute in the state parameter, such as the temperature range between 20-25 ℃ or the humidity range between 40-60%.

According to different business demands, the state parameters and command configuration parameters in the configured algorithm and the terminal equipment in the corresponding equipment system form a mapping relation, one algorithm can form a mapping relation with the state parameters and command configuration parameters of a plurality of equipment at the same time, and the regulation and control of the plurality of equipment are realized, so that the control and management are better carried out, and the control strategy and dimension are more advanced; meanwhile, the state parameters and command configuration parameters can be flexibly adjusted, the applicability of the equipment system is improved, and global control aiming at the optimization of the whole system performance is realized.

Optionally, referring to fig. 3, fig. 3 is a schematic flow chart of the present application for continuing to perform deployment, operation and monitoring of the algorithm after completing configuration of the algorithm, including:

step 30: running the configured algorithm at the cloud; or, deploying the configured algorithm to an edge server associated with the device system.

The algorithm after configuration has two deployment modes, one is deployed at the cloud end, and the other is deployed at the edge server; in the application, the algorithm deployed at the cloud end is deployed on an algorithm server, wherein the algorithm server is a linux server environment and is used for executing and monitoring an algorithm package; the algorithm server is equivalent to a cloud server, is a virtual server environment, provides infrastructure such as computing, storage, network and the like, and allows a user to deploy application programs and run various computing tasks on the algorithm server; in the application, the edge server is arranged on an edge computing server installed on the site of the equipment, and the edge computing server is a miniature computer device which has the edge computing capability and can provide necessary execution environment for the algorithm.

Two deployment modes are provided, one is deployed at the cloud end, the other is deployed at the edge server, remote deployment and updating of a control algorithm are realized, inconvenience brought by working personnel going to project field programming or updating of the algorithm is avoided, and therefore labor cost is reduced; and the method is deployed on the edge server, and even when the terminal equipment is in an offline state, the edge server can still process and respond to the algorithm request, so that service interruption caused by network connection problems is avoided.

Step 40: the running process of the algorithm is monitored.

After algorithm deployment and execution, an independent operation program is formed, wherein each independent operation program is an algorithm instance, and each algorithm instance has a unique ID number and is used for identification, tracking and monitoring; according to the ID number, basic information, configuration information, operation parameters, operation logs and command records of an algorithm can be queried, wherein the basic information comprises names of algorithm examples, first configuration information, operation states, deployment time, deployment personnel information and the like; the configuration information comprises second configuration information, wherein the second configuration information comprises a state parameter and a command configuration parameter; the operation parameters are used for judging whether the acquired operation log and command record accord with the setting or not, so that whether an algorithm operation process is wrong or not is determined, and if the operation parameters are wrong, prompt information is sent; the operation log records log information input in the algorithm operation process and is used for tracking the execution condition; the command record is used for recording the command record sent in the algorithm running process, and monitoring whether the terminal equipment successfully executes the action in the command configuration parameters of the algorithm.

Judging whether the operation process of the algorithm is normal or not by judging whether the information in the obtained log information and command record information accords with the setting of the algorithm under the operation parameters; therefore, the monitoring of the algorithm operation process can be realized, abnormal information in the algorithm operation process can be found in time, and the stability and reliability of the algorithm operation are improved.

Step 50: and when detecting that the algorithm runs wrong, sending out prompt information.

In the operation process of the algorithm, using interfaces provided by the system and the equipment data API, acquiring equipment state information of each terminal equipment in the equipment system according to state parameters in second configuration information of the algorithm, wherein the equipment state information comprises sensor data, energy consumption information and equipment operation state information acquired in real time; the sensor data comprise temperature, humidity or illumination information acquired by the current terminal equipment; the energy consumption information comprises the power consumption of each terminal device; the running state information of the equipment displays the running state of each terminal equipment in the equipment running state information display equipment system, wherein the running state can be information such as closing, running or abnormal running.

After the device state information of each terminal device is obtained, comparing the device state information with a command triggering parameter range included by the command configuration parameters in the second configuration information, and controlling each terminal device to execute a command action configured in an algorithm by using an interface provided by a device control API according to a comparison result, for example: when the host equipment in the water cooling system collects the current environment temperature to be 28 ℃ and the command triggering parameter range is set to be 20-25 ℃, the host equipment in the water cooling system can be remotely controlled to increase the running power through an interface provided by an equipment control API; or the current energy consumption of the equipment A in the multi-split system is larger, and the current area environment temperature of the equipment A is lower than the range of the command trigger parameters, so that the running power of the equipment A can be reduced or the equipment A can be closed remotely, and the energy consumption is reduced.

According to the acquired equipment operation state information, the operation state of each terminal equipment can be checked, and the real-time state monitoring of each terminal equipment is realized; when the current equipment running state information of the B equipment in the equipment system is obtained to be abnormal, firstly checking the algorithm running condition of the B equipment, and judging whether the running process of the algorithm is normal or not by judging whether the obtained information in the log information and the command record information accords with the setting of the algorithm under the running parameters or not; if the algorithm is in an abnormal state, prompt information is sent, a developer can determine the abnormal position of the algorithm according to the prompt information, and algorithm errors are timely modified; if the algorithm runs normally, judging that the terminal equipment has hardware faults, and informing maintenance personnel to overhaul the equipment according to the area where the equipment B is located.

The application realizes global control targeting the optimization of the whole system performance by collecting the state parameter information of each terminal device in the device system and comparing the state parameter information with the command configuration parameter information configured in the algorithm and remotely controlling the terminal device to execute the corresponding command configuration action according to the comparison result; meanwhile, the operation process of the algorithm is monitored, and prompt information is sent out when the operation error of the algorithm is monitored, so that the operation transparency of the algorithm is improved, and the stable operation of the algorithm is ensured.

Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a storage medium according to the present application.

The storage medium 60 stores program data 61, which program data 61, when executed by a processor, implements a cloud-based algorithm configuration method as described in fig. 1 to 3.

The program data 61 is stored in a storage medium 60 comprising instructions for causing a network device (which may be a router, personal computer, server, etc.) or processor to perform all or part of the steps of the method according to various embodiments of the application.

Alternatively, the storage medium 60 may be a usb disk, a removable hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk, or an optical disk, or other various media that can store the program data 61.

Referring to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of a cloud server according to the present application.

The cloud server 70 includes a memory 71 and a processor 72 connected to each other, wherein the memory 71 stores a computer program, and the processor 72 implements the cloud-based algorithm configuration method as described in fig. 1 to 3 when executing the computer program.

Referring to fig. 6, fig. 6 is a schematic structural diagram of an algorithm deployment system according to an embodiment of the present application.

The algorithm deployment system 80 includes a cloud server 70, a cloud database 81 and a device system 82 as described in fig. 5, wherein an algorithm is selected from the cloud database 81, and is configured on the cloud server 70, and deployed and operated, and the cloud server 70 is in communication connection with the device system 82.

Different from the prior art, the application discloses a cloud-based algorithm configuration method, a cloud server, a cloud system and a storage medium. The method comprises the following steps: responding to the received algorithm selection instruction and first configuration information, wherein the first configuration information comprises the system name and the area attribute of the equipment system so as to associate the selected algorithm with the corresponding equipment system; in response to receiving second configuration information for the algorithm, the second configuration information includes a state parameter and a command configuration parameter to form a mapping relationship of the state parameter and the command configuration parameter with each terminal device in the device system. By the method, remote deployment and updating of the control algorithm in the terminal equipment are realized, so that the labor cost is reduced; meanwhile, the applicability of the algorithm is improved, and the global control targeting on the optimization of the whole system performance is realized.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the storage medium embodiments and the electronic device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

In the embodiments provided in the present application, it should be understood that the disclosed method, storage medium, computer device and system may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The foregoing description is only illustrative of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present application.

Claims

1. The cloud-based algorithm configuration method is characterized by comprising the following steps of:

responding to a received algorithm selection instruction and first configuration information, wherein the first configuration information comprises a system name and a region attribute of a device system so as to associate the selected algorithm with the corresponding device system;

in response to receiving second configuration information for the algorithm, the second configuration information includes a state parameter and a command configuration parameter to form a mapping relationship between the state parameter and the command configuration parameter and each terminal device in the device system.

2. The cloud-based algorithm configuration method according to claim 1, further comprising, before the responding to the received algorithm selection instruction:

3. The cloud-based algorithm configuration method according to claim 2, comprising:

4. The cloud-based algorithm configuration method of claim 1, wherein the state parameters include device attributes, static attribute numbers, and power;

the command configuration parameters include a device control command and a command trigger parameter range.

5. The cloud-based algorithm configuration method of claim 1, further comprising, after the response to receiving the second configuration information for the algorithm:

6. The cloud-based algorithm configuration method of claim, further comprising:

monitoring the running process of the algorithm;

7. The cloud-based algorithm configuration method according to claim 6, wherein the monitoring the operation process of the algorithm comprises:

8. A storage medium having stored thereon program data, which when executed by a processor, implements the steps of the cloud-based algorithm configuration method of any of claims 1-7.

9. A cloud server comprising a processor and a memory, which are connected to each other, wherein the memory stores a computer program, and wherein the processor, when executing the computer program, performs the steps of the cloud-based algorithm configuration method according to any one of claims 1-7.

10. An algorithm deployment system comprising a device system, a cloud database and the cloud server of claim 9, the cloud database being associated with the cloud server, the cloud server being in communication with the device system.